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Source channel @githubtrending · Post #14993 · Jul 24

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#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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🎨 𝗧𝗘𝗠𝗔𝗦 𝗧𝗘𝗟𝗘𝗚𝗥𝗔𝗠 ➤ 𝗧𝗚𝗫

@dnz_temas · Post #1845 · 02/12/2023, 02:47 AM

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⠀ 📱 𝐃𝐍𝐙 𝐓𝐄𝐌𝐀𝐒 🔠🔠 >> 🔠🔠🔠 📱 📢 𝗖𝗵𝗮𝗻𝗻𝗲𝗹: @DNZ_Temas🇧🇷 #Sunflower#dark ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➡️𝐀𝐏𝐏𝐋𝐘 𝐓𝐇𝐄𝐌𝐄 📱 ( Telegram ) ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ➡️𝗕‌𝐀𝐂𝐊𝐆𝐑𝐎𝐔𝐍𝐃 ➖➖➖➖➖➖➖➖ 🖼𝐖𝐀𝐋𝐋𝐏𝐀𝐏𝐄𝐑 ➖➖➖➖➖➖➖ ⠀ ╚════ ღ 𝔻ℕℤ 𝕋𝔼𝕄𝔸𝕊 ღ ══╝

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🎨 𝗧𝗘𝗠𝗔𝗦 𝗧𝗘𝗟𝗘𝗚𝗥𝗔𝗠 ➤ 𝗧𝗚𝗫

@dnz_temas · Post #1982 · 03/12/2023, 11:01 AM

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📱 𝐃𝐍𝐙 𝐓𝐄𝐌𝐀𝐒 🔠🔠 >> 🔠🔠🔠 📱 📢 𝗖𝗵𝗮𝗻𝗻𝗲𝗹: @DNZ_Temas🇧🇷 #Sunflower#blur#White ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ⚠️𝐀𝐏𝐏𝐋𝐘 𝐓𝐇𝐄𝐌𝐄 📱 ( Telegram ) ➖➖➖➖➖➖➖➖➖➖➖➖➖ ⠀ ╚════ ღ 𝔻ℕℤ 𝕋𝔼𝕄𝔸𝕊 ღ ══╝

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